The display of the predicted values and residuals is controlled by the P, R, CLM, and CLI options in the MODEL statement. The P option causes PROC REG to display the observation number, the ID value (if an ID statement is used), the actual value, the predicted value, and the residual. The R, CLI, and CLM options also produce the items under the P option. Thus, P is unnecessary if you use one of the other options.

The R option requests more detail, especially about the residuals. The standard errors of the mean predicted value and the residual are displayed. The studentized residual, which is the residual divided by its standard error, is both displayed and plotted. A measure of influence, Cook’s , is displayed. Cook’s measures the change to the estimates that results from deleting each observation (Cook 1977, 1979). This statistic is very similar to DFFITS.

The CLM option requests that PROC REG display the % lower and upper confidence limits for the mean predicted values. This accounts for the variation due to estimating the parameters only. If you want a % confidence interval for observed values, then you can use the CLI option, which adds in the variability of the error term. The level can be specified with the ALPHA= option in the PROC REG or MODEL statement.

You can use these statistics in PLOT and PAINT statements. This is useful in performing a variety of regression diagnostics. For definitions of the statistics produced by these options, see Chapter 4, Introduction to Regression Procedures.

The following statements use the U.S. population data found in the section Polynomial Regression. The results are shown in Figure 73.32 and Figure 73.33.

   data USPop2;
      input Year @@;
      YearSq=Year*Year;
      datalines;
   2010 2020 2030
   ;
   data USPop2;
      set USPopulation USPop2;
   
   proc reg data=USPop2;
      id Year;
      model Population=Year YearSq / r cli clm;
   run;